Cooperators have low degrees
#Define degrees of isolation
isolationDegree = 2
#number of iterations per arm
iterations = 100
modelForPrediction = "random forest" #"linear" or "random forest"
# List of manipulating parameters of experiments
#L : number of rounds
#V : Visible or not
#A : Income of a rich-group subject
#B : Income of a poor-group subject
#R : Probability to be assigned to a rich group
#I : Number of the same-parameter trial
R = 0.5
I = 0
L = 10
trends.df = data.frame()
for(A in c(1150,700,500)){
for(V in c(0,1)){
V = V
A = A
if(A==1150){B = 200} #high inequality
if(A==700){B = 300} #low inequality
if(A==500){B = 500} #no inequality
if(modelForPrediction=="random forest"){
source(paste(rootdir,"R/models.R",sep="/"))
if(V==0){
model1<-model1.invisible(redo=FALSE)
model2<-model2.invisible(redo=FALSE)
model3<-model3(redo=FALSE)
}
if(V==1){
model1<-model1.visible(redo=FALSE)
model2<-model2.visible(redo=FALSE)
model3<-model3(redo=FALSE)
}
}
df.netIntLowDegree = data.frame(
coopFrac = NULL,
avgCoop = NULL,
avgCoopFinal = NULL,
percentIsolation = NULL,
isolation = NULL,
percentIsolationC = NULL,
percentIsolationD = NULL,
nCommunities = NULL,
communitySize = NULL,
assortativityInitial = NULL,
assortativityFinal = NULL,
conversionRate = NULL,
conversionToD = NULL,
conversionToC = NULL,
transitivity = NULL,
degree = NULL,
degreeC = NULL,
degreeD = NULL,
meanConversionToD = NULL,
meanConversionToC = NULL,
degreeLost = NULL,
degreeLostC = NULL,
degreeLostD = NULL
)
#Here, factionCoop=0 will be the control: no rearranging of nodes will take place
for(frac in c(0,-1,1)){
#nodes in the top fractionCoop degrees will automatically be a cooperator
fractionCoop = frac
coopFrac = NULL
avgCoop = NULL
homophilyC = NULL
homophilyD = NULL
heterophily = NULL
avgCoopFinal = NULL
percentIsolation = NULL
isolation = NULL
percentIsolationC = NULL
percentIsolationD = NULL
nCommunities = NULL
communitySize = NULL
assortativityInitial = NULL
assortativityFinal = NULL
conversionRate = NULL
conversionToD = NULL
conversionToC = NULL
transitivity = NULL
degree = NULL
degreeC = NULL
degreeD = NULL
meanConversionToD = NULL
meanConversionToC = NULL
degreeLost = NULL
degreeLostC = NULL
degreeLostD = NULL
avg_wealth = NULL
gini = NULL
for(m in c(1:iterations)){
# Section 1. NOTES, packages, and Parameters
#Importing library
library(igraph) # for network graphing
library(reldist) # for gini calculatio
library(boot) # for inv.logit calculation
#Two prefixed functions
#rank
rank1 = function(x) {rank(x,na.last=NA,ties.method="average")[1]} #a smaller value has a smaller rank.
#gini mean difference (a.k.a. mean difference: please refer to https://stat.ethz.ch/pipermail/r-help/2003-April/032782.html)
gmd = function(x) {
x1 = na.omit(x)
n = length(x1)
tmp = 0
for (i in 1:n) {
for (j in 1:n) {
tmp <- tmp + abs(x1[i]-x1[j])
}
}
answer = tmp/(n*n)
return(answer)
}
# List of fixed parameters of experiments (assumptions)
#Rewiring rate = 0.3
#GINI coefficient (can be known by A or B)
GINI = 0*as.numeric(A==500) + 0.2*as.numeric(A %in% c(700,850)) + 0.4*as.numeric(A ==1150)
#Collecting data frame (final output data frame)
result = data.frame(round=0:L,n_par=NA,n_A=NA,avg_coop=NA,avg_degree=NA,avg_wealth=NA,gini=NA,gmd=NA,avg_coop_A=NA,avg_degree_A=NA,avg_wealth_A=NA,gini_A=NA,gmd_A=NA,avg_coop_B=NA,avg_degree_B=NA,avg_wealth_B=NA,gini_B=NA,gmd_B=NA,isolation=NA,percentIsolation=NA,meanConversionToD=NA,meanConversionToC=NA,degreeLost=NA,degreeLostC=NA,degreeLostD=NA)
#_A is for a richer group and _B is for a poorer group
#####################################################
# Section 1.5: Practice rounds 1 to 2, to determine C/D in round 1
N = 17 # median of the number of participants over rounds.
node_rp0 = data.frame(ego_id=1:N, round=0)
node_import = node_rp0
for (k in 1:2){
node_rX = node_import #Importing data
node_rX$round = node_rX$round + 1
node_rX[is.na(node_rX$prev_degree)==1,"prev_degree"] = 0
node_rX[is.na(node_rX$prev_local_rate_coop)==1,"prev_local_rate_coop"] = 0
#Only this calculation needs to change from Round 1
if (k==1) {
node_rX$prob_coop = inv.logit(1.099471)
} else {
node_rX$prob_coop = inv.logit((-0.02339288) + (1.46068980)*as.numeric(node_rX$prev_coop==1))
}
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
node_rX$prev_coop = node_rX$coop
assign(paste("coop_rp",k, sep=""),node_rX$coop)
#For the loop
node_import = node_rX
}
#cooperation rate in the practice rounds
coop_rp = apply(cbind(coop_rp1,coop_rp2),1,mean)
#####################################################
# Section 2: Round 0 (Agents and environments)
#Node data generation
N = 17 # median of the number of participants over rounds.
node_r0 = data.frame(ego_id=1:N, round=0)
node_r0$coop_rp = ifelse(coop_rp==1,"C","D")
node_r0$group = sample(c("rich","poor"),N,replace=TRUE,prob=c(R,1-R)) #R is defined as the probability to be assigned to the rich group
node_r0$initial_wealth = ifelse(node_r0$group=="rich",A,B)
#Link data generation
ego_list = NULL
for (i in 1:N) { ego_list = c(ego_list,rep(i,N)) }
link_r0 = data.frame(ego_id=ego_list,alt_id=rep(1:N,N))
link_r0 = link_r0[(link_r0$ego_id < link_r0$alt_id),] #The link was bidirectional, and thus the half and self are omitted.
link_r0$connected = sample(0:1,dim(link_r0)[1],replace=TRUE,prob=c(0.7,0.3)) #Initial rewiring rate is fixed, 0.3
link_r0c_ego = link_r0[link_r0$connected==1,]
link_r0c_alt = link_r0[link_r0$connected==1,]
colnames(link_r0c_alt) = c("alt_id","ego_id","connected")
link_r0c = rbind(link_r0c_ego,link_r0c_alt) #this is bidirectional (double counted) for connected ties.
link_r0c = link_r0c[order(link_r0c$ego_id),]
link_r0c$alternumber = NA #putting the number for each alter in the same ego
link_r0c[1,]$alternumber = 1
for (i in 1:(dim(link_r0c)[1]-1))
{if (link_r0c[i,]$ego_id == link_r0c[i+1,]$ego_id)
{link_r0c[i+1,]$alternumber = link_r0c[i,]$alternumber + 1}
else
{link_r0c[i+1,]$alternumber = 1}
#print(i)
}
link_r0c2 = reshape(link_r0c, direction = "wide", idvar=c("ego_id","connected"), timevar="alternumber")
link_r0c2$initial_degree = apply(link_r0c2[,colnames(link_r0c2)[substr(colnames(link_r0c2),1,6) == "alt_id"]],1,function(x){length(na.omit(x))}) #Degree of each ego
link_r0c2[is.na(link_r0c2$initial_degree)==1,"initial_degree"] = 0
#Reflect the degree and initial local gini coefficient into the node data
node_r0 = merge(x=node_r0,y=link_r0c2,all.x=TRUE,all.y=FALSE,by="ego_id")
node_r0$initial_avg_env_wealth = NA
node_r0$initial_local_gini = NA #local gini coefficient of the ego and connecting alters
node_r0$initial_rel_rank = NA #local rank of ego among the ego and connecting alters (divided by the number of the go and connecting alters)
for (i in 1:(dim(node_r0)[1])){
node_r0[i,]$initial_avg_env_wealth = mean(na.omit(node_r0[node_r0$ego_id %in%
node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6) %in% c("ego_id","alt_id")]],"initial_wealth"]))
node_r0[i,]$initial_local_gini = gini(na.omit(node_r0[node_r0$ego_id %in% node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6)
%in% c("ego_id","alt_id")]],"initial_wealth"]))
node_r0[i,]$initial_rel_rank = rank1(na.omit(node_r0[node_r0$ego_id %in% node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6)
%in% c("ego_id","alt_id")]],"initial_wealth"]))/length(na.omit(node_r0[node_r0$ego_id %in%
node_r0[i,colnames(node_r0)[substr(colnames(node_r0),1,6) %in% c("ego_id","alt_id")]],"initial_wealth"]))
}
#Finalization of round 0 and Visualization
#plot(graph.data.frame(link_r0[link_r0$connected==1,],directed=F)) #plot.igraph
node_r0$everIsolated = 0
node_r0$maxDegreeLost = NA
result[result$round==0,2:25] = c(length(node_r0$ego_id),length(node_r0[node_r0$group=="rich",]$ego_id),NA,mean(node_r0$initial_degree),mean(node_r0$initial_wealth),gini(node_r0$initial_wealth),gmd(node_r0$initial_wealth),NA,mean(node_r0[node_r0$group=="rich",]$initial_degree),mean(node_r0[node_r0$group=="rich",]$initial_wealth),gini(node_r0[node_r0$group=="rich",]$initial_wealth),gmd(node_r0[node_r0$group=="rich",]$initial_wealth),NA,mean(node_r0[node_r0$group=="poor",]$initial_degree),mean(node_r0[node_r0$group=="poor",]$initial_wealth),gini(node_r0[node_r0$group=="poor",]$initial_wealth),gmd(node_r0[node_r0$group=="poor",]$initial_wealth),
as.numeric(ifelse(is.na(table(node_r0$initial_degree<=isolationDegree)["TRUE"]),0,1)),
as.numeric(sum(node_r0$everIsolated)/length(node_r0$ego_id)),
NA,
NA,
NA,NA,NA
)
#For the loop at the next round (for round 1, the initial one is the same as the previous [1 prior] one)
node_import = node_r0
node_import$initial_coop = NA
node_import$prev_coop = NA
node_import$prev_wealth = node_import$initial_wealth
node_import$prev_degree = node_import$initial_degree
node_import$prev_avg_env_wealth = node_import$initial_avg_env_wealth
node_import$prev_local_gini = node_import$initial_local_gini
node_import$prev_rel_rank = node_import$initial_rel_rank
node_import$prev_local_rate_coop = NA
link_import = link_r0
#####################################################
# Section 3: Rounds 1 to 10 or more (behaviors in simulation: the equation of cooperation is different at round 1 because of no history)
#3-1: Cooperation phase
for (k in 1:L)
{
node_rX = node_import #Importing data
node_rX$round = node_rX$round + 1
node_rX[is.na(node_rX$prev_degree)==1,"prev_degree"] = 0
node_rX[is.na(node_rX$prev_local_rate_coop)==1,"prev_local_rate_coop"] = 0
#Only this calculation needs to change from Round 1
if(modelForPrediction=="linear"){
if (k==1) {
node_rX$prob_coop = as.numeric(V==0)*inv.logit((-1.816665) + (2.086067)*coop_rp1 + (1.800153)*coop_rp2) + as.numeric(V==1)*inv.logit((-2.031577) + (2.427157)*coop_rp1 + (1.684193)*coop_rp2 + (-1.528851)*GINI)
} else {
node_rX$prob_coop = as.numeric(V==0 & node_rX$prev_coop==0)*inv.logit(-1.039916) + as.numeric(V==0 & node_rX$prev_coop==1)*inv.logit(2.062023) + as.numeric(V==1 & node_rX$prev_coop==0)*inv.logit((-0.2574838)*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-1.214198)*GINI + (2.508148)*GINI*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-0.9749075)) + as.numeric(V==1 & node_rX$prev_coop==1)*inv.logit((- 0.6197254)*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (-0.7480261)*GINI + (1.169674)*GINI*as.numeric(node_rX$prev_avg_env_wealth - node_rX$prev_wealth > 0) + (1.356784))
}
}
if(modelForPrediction=="random forest"){
if (k==1) {
if(V==1){node_rX$prob_coop = predict(model1,
newdata=
data.frame(
behavior.p1 = coop_rp1,
behavior.p2 = coop_rp2,
gini = GINI
),
type = "prob"
)[[1]]$C}
else if(V==0){node_rX$prob_coop = predict(model1,
newdata=
data.frame(
behavior.p1 = coop_rp1,
behavior.p2 = coop_rp2
),
type = "prob"
)[[1]]$C}
} else {
if(V==1){node_rX$prob_coop = predict(model2,
newdata=
data.frame(
prevCoop = node_rX$prev_coop,
gini = GINI,
alterPrevWealth = node_rX$prev_avg_env_wealth,
egoPrevWealth = node_rX$prev_wealth
),
type = "prob"
)[[1]]$C}
else if(V==0){node_rX$prob_coop = predict(model2,
newdata=
data.frame(
prevCoop = node_rX$prev_coop,
alterPrevWealth = node_rX$prev_avg_env_wealth,
egoPrevWealth = node_rX$prev_wealth
),
type = "prob"
)[[1]]$C}
}
}
#####rearrange node degrees before round 1 depending on cooperation in practice rounds!
if(k==1){
if(fractionCoop==0){
node_rX$prob_coop
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
coop_rp_init = coop_rp
}
if(fractionCoop==-1){
prob_coop_df = NULL
nodesCoop = NULL
#match highest degree nodes with most cooperative people
prob_coop_df =
data.frame(
prob_coop = rev(node_rX$prob_coop[order(coop_rp)]), #order nodes from most cooperative to least cooperative
node_number = rev(order(node_rX$prev_degree)) #order nodes from largest degree to smallest degree
)
node_rX$prob_coop = prob_coop_df[order(prob_coop_df$node_number),]$prob_coop
#coop_rp of the rearranged nodes
coop_rp_init = rev(coop_rp[order(coop_rp)])[order(prob_coop_df$node_number)]
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
}
if(fractionCoop==1){
prob_coop_df = NULL
nodesCoop = NULL
#match highest degree nodes with least cooperative people
prob_coop_df =
data.frame(
prob_coop = rev(node_rX$prob_coop[order(coop_rp)]), #order nodes from most cooperative to least cooperative
node_number = order(node_rX$prev_degree) #order nodes from smallest degree to largest degree
)
node_rX$prob_coop = prob_coop_df[order(prob_coop_df$node_number),]$prob_coop
#coop_rp of the rearranged nodes
coop_rp_init = rev(coop_rp[order(coop_rp)])[order(prob_coop_df$node_number)]
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
}
} else {
node_rX$coop = apply(data.frame(node_rX$prob_coop),1,function(x) {sample(1:0,1,prob=c(x,(1-x)))})
}
if (k==1) {
node_rX$initial_coop = node_rX$coop
} else {
node_rX$initial_coop = node_rX$initial_coop
}
node_rX$cost = (-50)*node_rX$coop*node_rX$prev_degree
node_rX$n_coop_received = NA
for (i in 1:(dim(node_rX)[1]))
{
node_rX[i,]$n_coop_received = sum(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) ==
"alt_id"]],"coop"])
}
node_rX$benefit = 100*node_rX$n_coop_received
node_rX$payoff = node_rX$cost + node_rX$benefit
node_rX$wealth = node_rX$prev_wealth + node_rX$payoff
node_rX$rel_rank = NA
node_rX$local_rate_coop = NA
for (i in 1:dim(node_rX)[1])
{
node_rX[i,]$rel_rank = rank1(na.omit(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in%
c("ego_id","alt_id")]],"wealth"]))/length(na.omit(node_rX[node_rX$ego_id %in%
node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX[i,]$local_rate_coop = mean(na.omit(node_rX[node_rX$ego_id %in% node_rX[i,colnames(node_rX)[substr(colnames(node_rX),1,6) %in%
c("ego_id","alt_id")]],"coop"]))
}
node_rX$growth = as.numeric((node_rX$wealth/node_rX$prev_wealth) > 1)
node_rX = node_rX[,c("ego_id","round","group","prev_degree","initial_wealth","initial_local_gini","initial_coop","coop","wealth","rel_rank","local_rate_coop","growth","everIsolated","maxDegreeLost")] #Pruning the previous-round data (degree is not updating yet)
#3-2: Rewiring phase
# 30% of ties (unidirectional) are being rewired
link_rX_1 = link_import #Importing data (bidirectioanl ego-alter [ego_id < alter_id])
colnames(link_rX_1) = c("ego_id","alt_id","prev_connected")
link_rX_1$challenge = sample(0:1,dim(link_rX_1)[1],replace=TRUE,prob=c(0.7,0.3)) # The bidirectional ties being rewired are selected (rewiring rate = 0.3).
ego_node_data =
node_rX[,c("ego_id","wealth","coop","prev_degree","initial_wealth","initial_local_gini","initial_coop","rel_rank","local_rate_coop","growth")]
colnames(ego_node_data) =
c("ego_id","ego_wealth","ego_coop","ego_prev_degree","ego_initial_wealth","ego_initial_local_gini","ego_initial_coop","ego_rel_rank","ego_local_rate_coop","ego_growth")
alt_node_data =
node_rX[,c("ego_id","wealth","coop","prev_degree","initial_wealth","initial_local_gini","initial_coop","rel_rank","local_rate_coop","growth")]
colnames(alt_node_data) =
c("alt_id","alt_wealth","alt_coop","alt_prev_degree","alt_initial_wealth","alt_initial_local_gini","alt_initial_coop","alt_rel_rank","alt_local_rate_coop","alt_growth")
link_rX_2 = merge(x=link_rX_1,y=ego_node_data,all.x=TRUE,all.y=FALSE,by="ego_id")
link_rX_3 = merge(x=link_rX_2,y=alt_node_data,all.x=TRUE,all.y=FALSE,by="alt_id")
link_rX_3$choice = sample(c("ego","alt"),dim(link_rX_3)[1],replace=TRUE,prob=c(0.5,0.5)) #decision maker for breaking a link, which is a unilateral decision
#ego_prob: probability of choosing to connect when challenged (asked)
if(modelForPrediction=="linear"){
link_rX_3$ego_prob = inv.logit((0.5134401)*link_rX_3$prev_connected + (-0.852406)*link_rX_3$ego_coop + (2.96549)*link_rX_3$alt_coop + (-0.1808545))
link_rX_3$alt_prob = inv.logit((0.5134401)*link_rX_3$prev_connected + (-0.852406)*link_rX_3$alt_coop + (2.96549)*link_rX_3$ego_coop + (-0.1808545))}
if(modelForPrediction=="random forest"){
link_rX_3$ego_prob = predict(model3,
newdata=
data.frame(
previouslyconnected = link_rX_3$prev_connected,
ego_behavior = link_rX_3$ego_coop,
alter_behavior = link_rX_3$alt_coop
),
type = "prob"
)[[1]]$C
link_rX_3$alt_prob = predict(model3,
newdata=
data.frame(
previouslyconnected = link_rX_3$prev_connected,
ego_behavior = link_rX_3$alt_coop,
alter_behavior = link_rX_3$ego_coop
),
type = "prob"
)[[1]]$C
}
link_rX_3$prob_connect = ifelse(link_rX_3$prev_connected == 1, ifelse(link_rX_3$choice == "ego", link_rX_3$ego_prob,
link_rX_3$alt_prob), link_rX_3$ego_prob*link_rX_3$alt_prob)
link_rX_3$connect_update = apply(data.frame(link_rX_3$prob_connect),1, function(x) {sample(1:0,1,prob=c(x,(1-x)))})
link_rX_3$connected = ifelse(link_rX_3$challenge==0,link_rX_3$prev_connected,link_rX_3$connect_update)
link_rX = link_rX_3[,c("ego_id","alt_id","connected")] #pruning and data is updated
#Reflect the degree and local gini coefficient into the node data
link_rXc_ego = link_rX[link_rX$connected==1,]
link_rXc_alt = link_rX[link_rX$connected==1,]
colnames(link_rXc_alt) = c("alt_id","ego_id","connected")
link_rXc = rbind(link_rXc_ego,link_rXc_alt)
link_rXc = link_rXc[order(link_rXc$ego_id),]
link_rXc$alternumber = NA
link_rXc[1,]$alternumber = 1
for (i in 1:(dim(link_rXc)[1]-1))
{
if (link_rXc[i,]$ego_id == link_rXc[i+1,]$ego_id)
{
link_rXc[i+1,]$alternumber = link_rXc[i,]$alternumber + 1
}
else
{
link_rXc[i+1,]$alternumber = 1
}
#print(i)
}
link_rXc2 = reshape(link_rXc, direction = "wide", idvar=c("ego_id","connected"), timevar="alternumber")
link_rXc2$degree = apply(link_rXc2[,colnames(link_rXc2)[substr(colnames(link_rXc2),1,3) == "alt"]],1,function(x) {length(na.omit(x))})
node_rX_final = merge(x=node_rX[,c("ego_id","round","group","initial_wealth","initial_local_gini","initial_coop","coop","wealth","growth","everIsolated","maxDegreeLost")],y=link_rXc2,all.x=TRUE,all.y=FALSE,by="ego_id")
node_rX_final[is.na(node_rX_final$degree)==1,"degree"] = 0
node_rX_final$avg_env_wealth = NA
node_rX_final$local_gini = NA #needs to be updated because the social network changes at the rewiring phase
node_rX_final$local_rate_coop = NA
node_rX_final$rel_rank = NA
for (i in 1:dim(node_rX_final)[1])
{
node_rX_final[i,]$avg_env_wealth = mean(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$local_gini = gini(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$local_rate_coop = mean(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"coop"]))
node_rX_final[i,]$rel_rank = rank1(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in%
c("ego_id","alt_id")]],"wealth"]))/length(na.omit(node_rX_final[node_rX_final$ego_id %in%
node_rX_final[i,colnames(node_rX_final)[substr(colnames(node_rX_final),1,6) %in% c("ego_id","alt_id")]],"wealth"]))
node_rX_final[i,]$everIsolated = ifelse(node_rX_final[i,]$everIsolated==1,1,ifelse(node_rX_final[i,]$degree<=isolationDegree,1,0))
node_rX_final[i,]$maxDegreeLost = pmax(node_r0[i,]$initial_degree - node_rX_final[i,]$degree, node_rX_final[i,]$maxDegreeLost, na.rm=TRUE)
}
#Finalization of round X and Visualization
#plot(graph.data.frame(link_rX[link_rX$connected==1,],directed=F)) #plot.igraph
result[result$round==k,2:25] =
c(length(node_rX_final$ego_id),length(node_rX_final[node_rX_final$group=="rich",]$ego_id),mean(node_rX_final$coop),mean(node_rX_final$degree),mean(node_rX_final$wealth),gini(node_rX_final$wealth),gmd(node_rX_final$wealth),mean(node_rX_final[node_rX_final$group=="rich",]$coop),mean(node_rX_final[node_rX_final$group=="rich",]$degree),mean(node_rX_final[node_rX_final$group=="rich",]$wealth),gini(node_rX_final[node_rX_final$group=="rich",]$wealth),gmd(node_rX_final[node_rX_final$group=="rich",]$wealth),mean(node_rX_final[node_rX_final$group=="poor",]$coop),mean(node_rX_final[node_rX_final$group=="poor",]$degree),mean(node_rX_final[node_rX_final$group=="poor",]$wealth),gini(node_rX_final[node_rX_final$group=="poor",]$wealth),gmd(node_rX_final[node_rX_final$group=="poor",]$wealth),
as.numeric(ifelse(is.na(table(node_rX_final$degree<=isolationDegree)["TRUE"]),0,1)),
as.numeric(sum(node_rX_final$everIsolated)/length(node_rX_final$ego_id)),
prop.table(table(node_rX_final[node_rX_final$initial_coop==1]$coop))["0"],
prop.table(table(node_rX_final[node_rX_final$initial_coop==0]$coop))["1"],
suppressWarnings({mean(node_rX_final$maxDegreeLost,na.rm=TRUE)}),
suppressWarnings({mean(node_rX_final[node_rX_final$initial_coop==1]$maxDegreeLost,na.rm=TRUE)}),
suppressWarnings({mean(node_rX_final[node_rX_final$initial_coop==0]$maxDegreeLost,na.rm=TRUE)})
)
#For the loop
node_import = node_rX_final
colnames(node_import)[colnames(node_import) %in%
c("coop","wealth","growth","degree","avg_env_wealth","local_gini","local_rate_coop","rel_rank")] =
c("prev_coop","prev_wealth","prev_growth","prev_degree","prev_avg_env_wealth","prev_local_gini","prev_local_rate_coop","prev_rel_rank")
link_import = link_rX
#print(paste0("Round ",k," is done."))
}
trends.df = rbind(trends.df,cbind(result[c("round","gini","gmd","avg_wealth","avg_coop","avg_degree")],V,GINI,fractionCoop))
link_rX_final = data.table::melt(setDT(node_rX_final),
measure = patterns('alt_id'),
variable.name = 'linkNumber',
value.name = c('alt_id'))
link_rX_final = data.frame(link_rX_final)[c("ego_id","alt_id")]
link_rX_final = link_rX_final[complete.cases(link_rX_final),]
link_rX_final = data.frame(t(unique(apply(link_rX_final, 1, function(x) sort(x))))) %>% distinct(X1, X2)
node_g_final = data.frame(node_rX_final)[c("ego_id","initial_coop","coop")]
node_g_final$initial_coop = factor(node_g_final$initial_coop)
g_rX_final = graph_from_data_frame(link_rX_final, directed = FALSE, vertices=node_g_final)
g_r0 = graph_from_data_frame(link_r0[link_r0$connected==1,][1:2], directed = FALSE, vertices=node_r0)
E(g_r0)$coopEdgeC = sapply(E(g_r0), function(e) prod(ifelse(V(g_r0)[inc(e)]$coop_rp=="C",1,0)))
E(g_r0)$coopEdgeD = sapply(E(g_r0), function(e) prod(ifelse(V(g_r0)[inc(e)]$coop_rp=="D",1,0)))
E(g_r0)$coopEdgeCD = sapply(E(g_r0), function(e) ifelse(sum(ifelse(V(g_r0)[inc(e)]$coop_rp=="C",1,0))==1,1,0))
#C-assortativity, defined as number of observed C-C edges out of total possible C-C edges
homophilyC[m] = sum(E(g_r0)$coopEdgeC) / (table(V(g_r0)$coop_rp)["C"]*(table(V(g_r0)$coop_rp)["C"]-1)/2)
#D-assortativity, defined as number of observed C-C edges out of total possible C-C edges
homophilyD[m] = sum(E(g_r0)$coopEdgeD) / (table(V(g_r0)$coop_rp)["D"]*(table(V(g_r0)$coop_rp)["D"]-1)/2)
#heterophily, defined as number of observed C-D edges out of total possible C-D edges
heterophily[m] = sum(E(g_r0)$coopEdgeCD) / (table(V(g_r0)$coop_rp)["C"]*table(V(g_r0)$coop_rp)["D"])
coopFrac[m] = fractionCoop
avgCoop[m] = prop.table(table(V(g_r0)$coop_rp))["C"]
avgCoopFinal[m] = result[result$round==10,]$avg_coop
percentIsolation[m] = max(result[result$round>=1,]$percentIsolation)
isolation[m] = max(result[result$round>=1,]$isolation)
#percentage of isolation among those who cooperated in both practice rounds
percentIsolationC[m] = sum(node_rX_final[coop_rp_init==1,]$everIsolated)/length(node_rX_final[coop_rp_init==1,]$everIsolated)
#percentage of isolation among those who defected at least once in practice rounds
percentIsolationD[m] = sum(node_rX_final[coop_rp_init<=0.5,]$everIsolated)/length(node_rX_final[coop_rp_init<=0.5,]$everIsolated)
nCommunities[m] = max(membership(cluster_louvain(g_rX_final)),na.rm=TRUE)
communitySize[m] = mean(table(membership(cluster_louvain(g_rX_final))),na.rm=TRUE)
assortativityInitial[m] = assortativity(g_r0, V(g_r0)$coop_rp == "C")
assortativityFinal[m] = assortativity(g_rX_final, V(g_r0)$coop_rp == "C")
conversionRate[m] = prop.table(table(V(g_rX_final)$coop == ifelse(V(g_r0)$coop_rp=="C","1","0")))["FALSE"]
conversionToD[m] = prop.table(table(V(g_rX_final)$coop[V(g_r0)$coop_rp == "C"]))["0"]
conversionToC[m] = prop.table(table(V(g_rX_final)$coop[V(g_r0)$coop_rp == "C"]))["1"]
transitivity[m] = mean(transitivity(g_rX_final, type="global"),na.rm=TRUE)
degree[m] = mean(igraph::degree(g_rX_final),na.rm=TRUE)
degreeC[m] = mean(igraph::degree(g_r0)[coop_rp_init==1],na.rm=TRUE)
degreeD[m] = mean(igraph::degree(g_r0)[coop_rp_init<=0.5],na.rm=TRUE)
meanConversionToD[m] = mean(result[result$round>=2,]$meanConversionToD, na.rm=TRUE)
meanConversionToC[m] = mean(result[result$round>=2,]$meanConversionToC, na.rm=TRUE)
degreeLost[m] = result[result$round==10,]$degreeLost
degreeLostC[m] = result[result$round==10,]$degreeLostC
degreeLostD[m] = result[result$round==10,]$degreeLostD
avg_wealth[m] = result[result$round==10,]$avg_wealth
gini[m] = result[result$round==10,]$gini
}
df.netIntLowDegree = rbind(df.netIntLowDegree,
data.frame(
coopFrac = coopFrac,
avgCoop = avgCoop,
avgCoopFinal = avgCoopFinal,
percentIsolation = percentIsolation,
isolation = isolation,
percentIsolationC = percentIsolationC,
percentIsolationD = percentIsolationD,
nCommunities = nCommunities,
communitySize = communitySize,
assortativityInitial = assortativityInitial,
assortativityFinal = assortativityFinal,
conversionRate = conversionRate,
conversionToD = conversionToD,
conversionToC = conversionToC,
homophilyC = homophilyC,
homophilyD = homophilyD,
heterophily = heterophily,
transitivity = transitivity,
degree = degree,
degreeC = degreeC,
degreeD = degreeD,
meanConversionToD = meanConversionToD,
meanConversionToC = meanConversionToC,
degreeLost = degreeLost,
degreeLostC = degreeLostC,
degreeLostD = degreeLostD,
avg_wealth = avg_wealth,
gini = gini
))
#plot(g_r0,vertex.color=V(g_rX_final)$initial_coop,vertex.label=ifelse(is.na(V(g_rX_final)$initial_coop),"NA",ifelse(V(g_rX_final)$initial_coop==1,"C","D")),main=paste("fracCoop=",frac,", round 0",sep=""))
#plot(g_rX_final,vertex.color=V(g_rX_final)$initial_coop,vertex.label=ifelse(is.na(V(g_rX_final)$initial_coop),"NA",ifelse(V(g_rX_final)$initial_coop==1,"C","D")),main=paste("fracCoop=",frac,", final round",sep=""))
}
sum.netIntLowDegree <- data.frame(
df.netIntLowDegree %>%
group_by(coopFrac) %>%
summarise(
mean.isolation = mean(isolation),
ci.isolation = 1.96 * sd(isolation)/sqrt(n()),
mean.percentIsolation = mean(percentIsolation),
ci.percentIsolation = 1.96 * sd(percentIsolation)/sqrt(n()),
mean.percentIsolationC = mean(percentIsolationC,na.rm=TRUE),
ci.percentIsolationC = 1.96 * sd(percentIsolationC,na.rm=TRUE)/sqrt(sum(isolation)),
mean.percentIsolationD = mean(percentIsolationD,na.rm=TRUE),
ci.percentIsolationD = 1.96 * sd(percentIsolationD,na.rm=TRUE)/sqrt(sum(isolation)),
mean.avgCoop = mean(avgCoop,na.rm=TRUE),
ci.avgCoop = 1.96 * sd(avgCoop,na.rm=TRUE)/sqrt(n()),
mean.avgCoopFinal = mean(avgCoopFinal,na.rm=TRUE),
ci.avgCoopFinal = 1.96 * sd(avgCoopFinal,na.rm=TRUE)/sqrt(n()),
mean.nCommunities = mean(nCommunities,na.rm=TRUE),
ci.nCommunities = 1.96 * sd(nCommunities,na.rm=TRUE)/sqrt(n()),
mean.communitySize = mean(communitySize,na.rm=TRUE),
ci.communitySize = 1.96 * sd(communitySize,na.rm=TRUE)/sqrt(n()),
mean.assortativityInitial = mean(assortativityInitial,na.rm=TRUE),
ci.assortativityInitial = 1.96 * sd(assortativityInitial,na.rm=TRUE)/sqrt(n()),
mean.assortativityFinal = mean(assortativityFinal,na.rm=TRUE),
ci.assortativityFinal = 1.96 * sd(assortativityFinal,na.rm=TRUE)/sqrt(n()),
mean.conversionRate = mean(conversionRate,na.rm=TRUE),
ci.conversionRate = 1.96 * sd(conversionRate,na.rm=TRUE)/sqrt(n()),
mean.conversionToD = mean(conversionToD,na.rm=TRUE),
ci.conversionToD = 1.96 * sd(conversionToD,na.rm=TRUE)/sqrt(n()),
mean.conversionToC = mean(conversionToC,na.rm=TRUE),
ci.conversionToC = 1.96 * sd(conversionToC,na.rm=TRUE)/sqrt(n()),
mean.homophilyC = mean(homophilyC,na.rm=TRUE),
ci.homophilyC = 1.96 * sd(homophilyC,na.rm=TRUE)/sqrt(n()),
mean.homophilyD = mean(homophilyD,na.rm=TRUE),
ci.homophilyD = 1.96 * sd(homophilyD,na.rm=TRUE)/sqrt(n()),
mean.heterophily = mean(heterophily,na.rm=TRUE),
ci.heterophily = 1.96 * sd(heterophily,na.rm=TRUE)/sqrt(n()),
mean.transitivity = mean(transitivity,na.rm=TRUE),
ci.transitivity = 1.96 * sd(transitivity,na.rm=TRUE)/sqrt(n()),
mean.degree = mean(degree,na.rm=TRUE),
ci.degree = 1.96 * sd(degree,na.rm=TRUE)/sqrt(n()),
mean.degreeC = mean(degreeC,na.rm=TRUE),
ci.degreeC = 1.96 * sd(degreeC,na.rm=TRUE)/sqrt(n()),
mean.degreeD = mean(degreeD,na.rm=TRUE),
ci.degreeD = 1.96 * sd(degreeD,na.rm=TRUE)/sqrt(n()),
mean.meanConversionToD = mean(meanConversionToD,na.rm=TRUE),
ci.meanConversionToD = 1.96 * sd(meanConversionToD,na.rm=TRUE)/sqrt(n()),
mean.meanConversionToC = mean(meanConversionToC,na.rm=TRUE),
ci.meanConversionToC = 1.96 * sd(meanConversionToC,na.rm=TRUE)/sqrt(n()),
mean.degreeLost = mean(degreeLost,na.rm=TRUE),
ci.degreeLost = 1.96 * sd(degreeLost,na.rm=TRUE)/sqrt(n()),
mean.degreeLostC = mean(degreeLostC,na.rm=TRUE),
ci.degreeLostC = 1.96 * sd(degreeLostC,na.rm=TRUE)/sqrt(n()),
mean.degreeLostD = mean(degreeLostD,na.rm=TRUE),
ci.degreeLostD = 1.96 * sd(degreeLostD,na.rm=TRUE)/sqrt(n()),
mean.avg_wealth = mean(avg_wealth,na.rm=TRUE),
ci.avg_wealth = 1.96 * sd(avg_wealth,na.rm=TRUE)/sqrt(n()),
mean.gini = mean(gini,na.rm=TRUE),
ci.gini = 1.96 * sd(gini,na.rm=TRUE)/sqrt(n())
)
)
kable(sum.netIntLowDegree[c(1:9)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,10:17)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,18:25)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,26:33)]) %>% kableExtra::kable_styling(font_size = 10)
kable(sum.netIntLowDegree[c(1,34:ncol(sum.netIntLowDegree))]) %>% kableExtra::kable_styling(font_size = 10)
compare_means(percentIsolation ~ coopFrac, data=df.netIntLowDegree)
compare_means(avgCoop ~ coopFrac, data=df.netIntLowDegree)
compare_means(avgCoopFinal ~ coopFrac, data=df.netIntLowDegree)
compare_means(nCommunities ~ coopFrac, data=df.netIntLowDegree)
compare_means(communitySize ~ coopFrac, data=df.netIntLowDegree)
compare_means(assortativityInitial ~ coopFrac, data=df.netIntLowDegree)
compare_means(assortativityFinal ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionRate ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionToD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(conversionToC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(meanConversionToD ~ coopFrac, data=df.netIntLowDegree)
#compare_means(meanConversionToC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLost ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLostC ~ coopFrac, data=df.netIntLowDegree)
#compare_means(degreeLostD ~ coopFrac, data=df.netIntLowDegree)
summary(lm(percentIsolation ~ assortativityInitial, data=df.netIntLowDegree))
#plot(df.netIntLowDegree$assortativityInitial, df.netIntLowDegree$percentIsolation)
#percentIsolation
g.percentIsolation = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolation", add = "mean_se", color="coopFrac") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.098, method="t.test", color="black") +
labs(
title = paste("Isolation, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=2, y=0.0990, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.10)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.percentIsolation)
#percentIsolationC
#percentage of isolation among those who cooperated in both practice rounds
g.percentIsolationC = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolationC", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.098, method="t.test", color="black") +
labs(
title = paste("Isolation among initial cooperators, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=2, y=0.0990, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.10)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.percentIsolationC)
#percentIsolationD
#percentage of isolation among those who defected at least once in practice rounds
g.percentIsolationD = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="percentIsolationD", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.298, method="t.test", color="black") +
labs(
title = paste("Isolation among initial defectors, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Propoption of ever-isolated individuals") +
annotate("text", x=2, y=0.2990, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.30)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.percentIsolationD)
#avgCoopFinal
g.avgCoopFinal = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="avgCoopFinal", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.98, method="t.test", color="black") +
labs(
title = paste("Cooperation in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Propoption of cooperators in final round") +
annotate("text", x=2, y=0.990, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,1.0)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.avgCoopFinal)
#avg_wealth
g.avg_wealth = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="avg_wealth", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 6800, method="t.test", color="black") +
labs(
title = paste("Wealth in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Average wealth in final round") +
annotate("text", x=2, y=6900, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,7000)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.avg_wealth)
#gini
g.gini = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="gini", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.48, method="t.test", color="black") +
labs(
title = paste("Gini coefficient in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Gini coefficient in final round") +
annotate("text", x=2, y=0.490, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,0.50)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.gini)
#degree
g.degree = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="degree", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 14.8, method="t.test", color="black") +
labs(
title = paste("Degree in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Mean degree in final round") +
annotate("text", x=2, y=14.90, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,15)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.degree)
#transitivity
g.transitivity = ggbarplot(data=df.netIntLowDegree, x="coopFrac", y="transitivity", add = "mean_se") +
stat_compare_means(ref.group = "0", label = "p.signif", label.y = 0.98, method="t.test", color="black") +
labs(
title = paste("Transitivity in final round, ","V=",V,", Gini=",GINI,sep=""),
x = "Node assignment",
y = "Transitivity in final round") +
annotate("text", x=2, y=0.99, label= "ref", color="black") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5, size=12),legend.position="none") +
coord_cartesian(ylim=c(0,1.00)) +
scale_x_discrete(labels=c('Highest to cooperators','Control','Highest to defectors')) +
scale_color_manual(values = c('0' = "black",'-1'="black",'1'="black"))
print(g.transitivity)
#initial C-assortativity
plotList <- lapply(
unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyC, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("C-assortativity, ","Control",sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
else{
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyC, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("C-assortativity, degree %ile = ",key,sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
}
)
plot= ggarrange(plotlist=plotList)
print(annotate_figure(plot, top = text_grob(paste("Proportion of ever-isolated individuals, ","V=",V,", Gini=", GINI, sep=""), color = "black", face = "bold", size = 10)))
lapply(unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
reg = lm(percentIsolation ~ homophilyC + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, ","Control"," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
else{
reg = lm(percentIsolation ~ homophilyC + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, degree %ile = ",key," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
}
)
#initial D-assortativity
plotList <- lapply(
unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyD, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("D-assortativity, ","Control",sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
else{
ggplot(data = df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,], aes(x = homophilyD, y = percentIsolation)) +
geom_point() +
scale_x_continuous(paste("D-assortativity, degree %ile = ",key,sep="")) +
scale_y_continuous("Proportion isolated") +
geom_smooth(method='lm', formula= y~x) +
stat_cor(method = "pearson")
}
}
)
plot= ggarrange(plotlist=plotList)
print(annotate_figure(plot, top = text_grob(paste("Proportion of ever-isolated individuals, ","V=",V,", Gini=", GINI, sep=""), color = "black", face = "bold", size = 10)))
lapply(unique(df.netIntLowDegree$coopFrac),
function(key) {
if(key==0){
reg = lm(percentIsolation ~ homophilyD + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, ","Control"," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
else{
reg = lm(percentIsolation ~ homophilyD + degreeD, data=df.netIntLowDegree[df.netIntLowDegree$coopFrac==key,])
print(paste("Regression on proportion of ever-isolated individuals, degree %ile = ",key," ; ",sep=""))
print(summary(reg)[4]$coefficients)
}
}
)
}
}
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.









## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.127424649 0.032429308 3.92930524 0.0001596773
## homophilyC 0.002440105 0.061434185 0.03971902 0.9683987775
## degreeD -0.016432743 0.005399661 -3.04329158 0.0030112976
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16932749 0.034025928 4.9764252 2.806272e-06
## homophilyC -0.04329336 0.100907959 -0.4290381 6.688469e-01
## degreeD -0.02004901 0.009239291 -2.1699731 3.245095e-02
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.090889166 0.03539215 2.5680603 0.01175277
## homophilyC -0.024247777 0.06772759 -0.3580192 0.72110686
## degreeD -0.007469483 0.00547882 -1.3633380 0.17593205

## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14004306 0.025637149 5.462505 3.623087e-07
## homophilyD 0.07914436 0.039192241 2.019389 4.620586e-02
## degreeD -0.02374496 0.006399727 -3.710309 3.452036e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16771900 0.028690852 5.845731 6.774539e-08
## homophilyD -0.04983562 0.048333323 -1.031082 3.050664e-01
## degreeD -0.01897685 0.008825619 -2.150201 3.402534e-02
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.087328804 0.034042319 2.5653013 0.01184052
## homophilyD -0.020489951 0.030093837 -0.6808687 0.49757672
## degreeD -0.007075152 0.005393416 -1.3118129 0.19267950
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.









## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13347226 0.031383484 4.2529459 4.861885e-05
## homophilyC -0.01216727 0.059452974 -0.2046536 8.382716e-01
## degreeD -0.02007060 0.005225526 -3.8408762 2.187312e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22534762 0.027496907 8.195381 1.029904e-12
## homophilyC -0.10675265 0.081545367 -1.309120 1.935865e-01
## degreeD -0.03752065 0.007466421 -5.025252 2.294737e-06
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.067375706 0.024830764 2.7133964 0.007881424
## homophilyC -0.041137316 0.047516975 -0.8657394 0.388769337
## degreeD -0.005857889 0.003843883 -1.5239506 0.130774810

## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13114668 0.025321182 5.1793271 1.208014e-06
## homophilyD 0.01103176 0.038709214 0.2849907 7.762589e-01
## degreeD -0.02102690 0.006320853 -3.3265921 1.242652e-03
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20929825 0.02341529 8.9385282 2.623051e-14
## homophilyD -0.03208556 0.03944598 -0.8134049 4.179781e-01
## degreeD -0.03969137 0.00720280 -5.5105477 2.944624e-07
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.061319933 0.023645421 2.593311 0.01097646
## homophilyD -0.036649935 0.020902848 -1.753346 0.08270157
## degreeD -0.005094462 0.003746207 -1.359899 0.17701448
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.









## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11361422 0.035069739 3.2396655 0.001639612
## homophilyC 0.04958449 0.066436227 0.7463471 0.457262822
## degreeD -0.01451210 0.005839308 -2.4852434 0.014657834
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15332961 0.035656713 4.3001612 4.068218e-05
## homophilyC -0.03381734 0.105744248 -0.3198031 7.498054e-01
## degreeD -0.01472992 0.009682109 -1.5213541 1.314243e-01
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.109480314 0.035082634 3.120641 0.002377358
## homophilyC -0.089477535 0.067135293 -1.332794 0.185721519
## degreeD -0.007767144 0.005430906 -1.430175 0.155880230

## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13251462 0.028356692 4.6731338 9.564554e-06
## homophilyD 0.01812981 0.043349686 0.4182225 6.767089e-01
## degreeD -0.01643381 0.007078598 -2.3216198 2.234590e-02
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15335681 0.030075376 5.0990819 1.689438e-06
## homophilyD -0.04857340 0.050665726 -0.9587033 3.400917e-01
## degreeD -0.01338098 0.009251514 -1.4463560 1.513010e-01
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09669133 0.033899558 2.852289 0.005304942
## homophilyD -0.03301096 0.029967634 -1.101554 0.273381624
## degreeD -0.00844272 0.005370798 -1.571968 0.119214359
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.









## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14635471 0.029370653 4.983025 2.731127e-06
## homophilyC -0.09328274 0.055639860 -1.676545 9.685163e-02
## degreeD -0.01780513 0.004890378 -3.640849 4.382253e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18548266 0.030314222 6.1186679 1.992587e-08
## homophilyC 0.05604893 0.089900451 0.6234555 5.344486e-01
## degreeD -0.04013457 0.008231426 -4.8757739 4.235079e-06
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.032418912 0.025783314 1.2573602 0.2116421
## homophilyC -0.028940065 0.049339806 -0.5865460 0.5588712
## degreeD -0.001253784 0.003991341 -0.3141259 0.7541000

## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12620437 0.023601611 5.347278 5.936426e-07
## homophilyD 0.06903944 0.036080457 1.913486 5.863446e-02
## degreeD -0.02370366 0.005891601 -4.023297 1.137240e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.197196884 0.025723496 7.6660218 1.365796e-11
## homophilyD -0.007859669 0.043334442 -0.1813723 8.564538e-01
## degreeD -0.037684891 0.007912828 -4.7625058 6.693275e-06
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02827358 0.024850032 1.1377685 0.2580197
## homophilyD -0.01176725 0.021967740 -0.5356603 0.5934195
## degreeD -0.00141775 0.003937057 -0.3601040 0.7195523
## Loading data last updated on 2023-01-20 20:37:15
## Call model1.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 20:39:47
## Call model2.invisible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.









## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.09825482 0.035507522 2.7671551 0.006772672
## homophilyC 0.04942249 0.067265563 0.7347369 0.464272317
## degreeD -0.01170399 0.005912201 -1.9796332 0.050578851
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.14875850 0.034818079 4.2724499 4.517451e-05
## homophilyC -0.02835795 0.103257177 -0.2746342 7.841814e-01
## degreeD -0.01419951 0.009454389 -1.5018961 1.363724e-01
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.126941143 0.040757921 3.114515 0.002422643
## homophilyC -0.142770096 0.077995711 -1.830486 0.070247802
## degreeD -0.007104183 0.006309459 -1.125957 0.262961612

## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12011247 0.028621297 4.1966118 6.004582e-05
## homophilyD 0.03828257 0.043754194 0.8749464 3.837644e-01
## degreeD -0.01548369 0.007144651 -2.1671728 3.267000e-02
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.15017116 0.029341063 5.118123 1.560566e-06
## homophilyD -0.05117593 0.049428684 -1.035349 3.030803e-01
## degreeD -0.01251457 0.009025631 -1.386558 1.687550e-01
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.106606009 0.039622302 2.690556 0.008400619
## homophilyD -0.044020599 0.035026612 -1.256776 0.211852814
## degreeD -0.008614857 0.006277467 -1.372346 0.173120882
## Loading data last updated on 2023-01-20 21:50:22
## Call model1.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 21:54:00
## Call model2.visible(redo=TRUE) to update data.
## Loading data last updated on 2023-01-20 22:02:59
## Call model3(redo=TRUE) to update data.









## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11918079 0.026587628 4.4825659 2.021535e-05
## homophilyC -0.01031574 0.050367688 -0.2048087 8.381507e-01
## degreeD -0.01724392 0.004426989 -3.8951795 1.803917e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16721218 0.028400813 5.8875840 5.623936e-08
## homophilyC -0.02239248 0.084226009 -0.2658618 7.909099e-01
## degreeD -0.02847758 0.007711865 -3.6926964 3.668344e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.050443709 0.022933738 2.1995415 0.03021486
## homophilyC -0.046759654 0.043886763 -1.0654614 0.28931209
## degreeD -0.003444311 0.003550218 -0.9701688 0.33437528

## [1] "Regression on proportion of ever-isolated individuals, Control ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.116054203 0.021460450 5.40781784 4.582779e-07
## homophilyD 0.001621072 0.032807203 0.04941206 9.606925e-01
## degreeD -0.017341531 0.005357109 -3.23710594 1.652927e-03
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = -1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16642273 0.024013096 6.9304988 4.638811e-10
## homophilyD -0.02609576 0.040453059 -0.6450875 5.203934e-01
## degreeD -0.02790609 0.007386691 -3.7778880 2.729325e-04
## [1] "Regression on proportion of ever-isolated individuals, degree %ile = 1 ; "
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.043707864 0.022059342 1.9813766 0.05037995
## homophilyD -0.023656323 0.019500735 -1.2130990 0.22803788
## degreeD -0.003476984 0.003494921 -0.9948678 0.32227591
plot.trends <-
data.frame(
trends.df %>%
group_by(round, V, GINI, fractionCoop) %>%
summarize_all(list(mean=~mean(., na.rm=TRUE),sd=~sd(., na.rm=TRUE)))
)
plot.trends$V = factor(plot.trends$V)
plot.trends$GINI = factor(plot.trends$GINI)
for(i in unique(plot.trends$fractionCoop)){
g.gini = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=gini_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = gini_mean - gini_sd, ymax = gini_mean + gini_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("gini") +
theme_bw()
g.gmd = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=gmd_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = gmd_mean - gmd_sd, ymax = gmd_mean + gmd_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("gmd") +
theme_bw()
g.avg_wealth = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_wealth_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_wealth_mean - avg_wealth_sd, ymax = avg_wealth_mean + avg_wealth_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_wealth") +
theme_bw()
g.avg_coop = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_coop_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_coop_mean - avg_coop_sd, ymax = avg_coop_mean + avg_coop_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_coop") +
theme_bw()
g.avg_degree = ggplot(data=plot.trends[plot.trends$fractionCoop==i,], aes(x=round,y=avg_degree_mean,group=interaction(GINI,V))) +
geom_line(aes(color=GINI,linetype=V)) +
geom_ribbon(aes(ymin = avg_degree_mean - avg_degree_sd, ymax = avg_degree_mean + avg_degree_sd, fill=GINI),alpha=0.3) +
xlab("Round")+
ylab("avg_degree") +
theme_bw()
plot <- ggarrange(g.gini,g.gmd,g.avg_wealth,g.avg_coop,g.avg_degree,common.legend = TRUE,legend="bottom")
print(annotate_figure(plot, top = text_grob(paste("Node assignment=",i), color = "black", face = "bold", size = 10)))
}
## Warning: Removed 6 rows containing missing values (`geom_line()`).
## Removed 6 rows containing missing values (`geom_line()`).

## Warning: Removed 6 rows containing missing values (`geom_line()`).


fig1 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyC, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig2 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyD, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig3 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = heterophily, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig4 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyC, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig5 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyD, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig6 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = heterophily, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
fig7 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = degreeD, color = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Mean degree of defectors") +
scale_color_viridis(option = "magma") +
labs(color="Isolated \nindividuals (%)")
print(ggarrange(fig1,fig2,fig3,fig4,fig5,fig6,fig7,common.legend = TRUE,legend="right"))

fig1 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Isolated individuals (%)")
fig2 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("Isolated individuals (%)")
fig3 = ggplot(data = df.netIntLowDegree,
aes(x = homophilyC, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("C-assortativity") +
scale_y_continuous("Isolated individuals (%)")
fig4 = ggplot(data = df.netIntLowDegree,
aes(x = homophilyD, y = percentIsolation*100)) +
geom_point() +
scale_x_continuous("D-assortativity") +
scale_y_continuous("Isolated individuals (%)")
print(ggarrange(fig1,fig2,fig3,fig4,common.legend = TRUE,legend="right"))

reg.isolation = glm(percentIsolation*100 ~ degreeC + degreeD + homophilyC + homophilyD + heterophily, data=df.netIntLowDegree, family = gaussian(link = "identity"))
summary(reg.isolation)
##
## Call:
## glm(formula = percentIsolation * 100 ~ degreeC + degreeD + homophilyC +
## homophilyD + heterophily, family = gaussian(link = "identity"),
## data = df.netIntLowDegree)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.7905 -2.6359 -0.4582 1.8254 12.6184
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6767 1.7221 7.361 1.84e-12 ***
## degreeC 0.6835 0.4182 1.634 0.1033
## degreeD -1.1624 0.2866 -4.056 6.40e-05 ***
## homophilyC -7.4766 4.5499 -1.643 0.1014
## homophilyD -3.0218 1.9769 -1.529 0.1275
## heterophily -11.1024 6.5337 -1.699 0.0903 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 16.3119)
##
## Null deviance: 6805.1 on 299 degrees of freedom
## Residual deviance: 4795.7 on 294 degrees of freedom
## AIC: 1696.9
##
## Number of Fisher Scoring iterations: 2
#variance inflation factor
car::vif(reg.isolation)
## degreeC degreeD homophilyC homophilyD heterophily
## 4.521076 3.715241 1.850618 1.441316 2.613732
reg.isolation = glm(percentIsolation*100 ~ degreeD + homophilyD, data=df.netIntLowDegree, family = gaussian(link = "identity"))
summary(reg.isolation)
##
## Call:
## glm(formula = percentIsolation * 100 ~ degreeD + homophilyD,
## family = gaussian(link = "identity"), data = df.netIntLowDegree)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -7.8155 -2.5232 -0.5326 1.8887 12.7180
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11.9541 0.8240 14.508 <2e-16 ***
## degreeD -1.5951 0.1531 -10.417 <2e-16 ***
## homophilyD -1.2500 1.6959 -0.737 0.462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 16.35011)
##
## Null deviance: 6805.1 on 299 degrees of freedom
## Residual deviance: 4856.0 on 297 degrees of freedom
## AIC: 1694.6
##
## Number of Fisher Scoring iterations: 2
#variance inflation factor
car::vif(reg.isolation)
## degreeD homophilyD
## 1.058209 1.058209
#double machine learning
library(DoubleML)
library(mlr3)
library(mlr3learners)
set.seed(3141)
##degreeC
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "degreeC",
x_cols = c("degreeD","homophilyC","homophilyD","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeC
## Covariates: degreeD, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 300
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeC
## Covariates: degreeD, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 300
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## degreeC 0.006509 0.003869 1.682 0.0925 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##degreeD
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "degreeD",
x_cols = c("degreeC","homophilyC","homophilyD","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeD
## Covariates: degreeC, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 300
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): degreeD
## Covariates: degreeC, homophilyC, homophilyD, heterophily
## Instrument(s):
## No. Observations: 300
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## degreeD -0.01240 0.00236 -5.253 1.49e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##homophilyC
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "homophilyC",
x_cols = c("degreeC","degreeD","homophilyD","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyC
## Covariates: degreeC, degreeD, homophilyD, heterophily
## Instrument(s):
## No. Observations: 300
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyC
## Covariates: degreeC, degreeD, homophilyD, heterophily
## Instrument(s):
## No. Observations: 300
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## homophilyC -0.04243 0.03918 -1.083 0.279
##homophilyD
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "homophilyD",
x_cols = c("degreeC","degreeD","homophilyC","heterophily"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyD
## Covariates: degreeC, degreeD, homophilyC, heterophily
## Instrument(s):
## No. Observations: 300
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): homophilyD
## Covariates: degreeC, degreeD, homophilyC, heterophily
## Instrument(s):
## No. Observations: 300
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## homophilyD -0.03744 0.01733 -2.16 0.0308 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##heterophily
dml_data = DoubleMLData$new(df.netIntLowDegree[complete.cases(df.netIntLowDegree[c("percentIsolation","degreeC","degreeD","homophilyC","homophilyD","heterophily")]),],
y_col = "percentIsolation",
d_cols = "heterophily",
x_cols = c("degreeC","degreeD","homophilyC","homophilyD"))
print(dml_data)
## ================= DoubleMLData Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): heterophily
## Covariates: degreeC, degreeD, homophilyC, homophilyD
## Instrument(s):
## No. Observations: 300
# surpress messages from mlr3 package during fitting
lgr::get_logger("mlr3")$set_threshold("warn")
learner = lrn("regr.ranger", num.trees=500, mtry=floor(sqrt(4)), max.depth=5, min.node.size=2)
ml_l = learner$clone()
ml_m = learner$clone()
obj_dml_plr = DoubleMLPLR$new(dml_data, ml_l=ml_l, ml_m=ml_m)
obj_dml_plr$fit()
print(obj_dml_plr)
## ================= DoubleMLPLR Object ==================
##
##
## ------------------ Data summary ------------------
## Outcome variable: percentIsolation
## Treatment variable(s): heterophily
## Covariates: degreeC, degreeD, homophilyC, homophilyD
## Instrument(s):
## No. Observations: 300
##
## ------------------ Score & algorithm ------------------
## Score function: partialling out
## DML algorithm: dml2
##
## ------------------ Machine learner ------------------
## ml_l: regr.ranger
## ml_m: regr.ranger
##
## ------------------ Resampling ------------------
## No. folds: 5
## No. repeated sample splits: 1
## Apply cross-fitting: TRUE
##
## ------------------ Fit summary ------------------
## Estimates and significance testing of the effect of target variables
## Estimate. Std. Error t value Pr(>|t|)
## heterophily -0.11929 0.06251 -1.908 0.0563 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
fig1 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyC, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig2 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = homophilyD, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig3 = ggplot(data = df.netIntLowDegree,
aes(x = degreeD, y = heterophily, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of defectors") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig4 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyC, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("C-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig5 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = homophilyD, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("D-assortativity") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig6 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = heterophily, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Heterophily") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
fig7 = ggplot(data = df.netIntLowDegree,
aes(x = degreeC, y = degreeD, color = avgCoopFinal*100)) +
geom_point() +
scale_x_continuous("Mean degree of cooperators") +
scale_y_continuous("Mean degree of defectors") +
scale_color_viridis(option = "magma") +
labs(color="Cooperation in \nfinal round (%)")
print(ggarrange(fig1,fig2,fig3,fig4,fig5,fig6,fig7,common.legend = TRUE,legend="right"))
